EGAD - Evolved Grasping Analysis Dataset

ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.25912/5eaa52c6eb6b4&rft.title=EGAD - Evolved Grasping Analysis Dataset for diversity and reproducibility in robotic manipulation&rft.identifier=10.25912/5eaa52c6eb6b4&rft.publisher=Queensland University of Technology&rft.description=The Evolved Grasping Analysis Dataset (EGAD), a collection of generated shapes for training and benchmarking robotic grasping and manipulatoin tasks.  Diverse and extensive training data are critical to training modern robotic systems to grasp, and yet many systems are trained on small or non-diverse datasets repurposed from other domains. We used evoluationary algorithms to create a set of objects which uniformly span the object space of simple to complex, and easy to difficult to grasp, with a focus on geometric diversity. The objects are all easily 3D-printable, making 1:1 sim-to-real transfer possible. Full details, code and videos can be found on the project website:  A preprint version of the paper can be found at:    &rft.creator=Morrison, Douglas &rft.creator=Corke, Peter &rft.creator=Leitner, Jurgen &rft.date=2020&rft.edition=1&rft.coverage=N/A&rft_rights=© Doug Morrison, Australian Center for Robotic Vision, Queensland University of Technology, 2020&rft_rights=BSD-3 (https://github.com/dougsm/egad/blob/master/LICENSE)&rft_subject=Artificial Intelligence and Image Processing not elsewhere classified&rft_subject=INFORMATION AND COMPUTING SCIENCES&rft_subject=ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING&rft.type=dataset&rft.language=English Access the data